{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 文件名：visualize_weather\n",
    "# 参数：无\n",
    "# 函数功能：读取csv文件，分析器中数据并绘制相应图像\n",
    "# 原作者：Randy Olson\n",
    "# 程序修改：西南科技大学 通信1701 刘仲夏小组\n",
    "# 修改时间：2020年8月29日\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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     "data": {
      "text/plain": "         date  Minimum Temperature(F)  Mean Temperature(F)  \\\n0  2014-07-01                    77.4                 81.0   \n1  2014-07-02                    78.3                 82.8   \n2  2014-07-03                    82.8                 84.3   \n3  2014-07-04                    82.4                 84.3   \n4  2014-07-05                    82.2                 83.7   \n5  2014-07-06                    82.8                 83.4   \n6  2014-07-07                    81.9                 83.5   \n7  2014-07-08                    81.3                 83.2   \n8  2014-07-09                    76.3                 81.1   \n9  2014-07-10                    78.3                 81.8   \n10 2014-07-11                    80.2                 83.4   \n11 2014-07-12                    81.9                 83.8   \n12 2014-07-13                    75.7                 80.4   \n13 2014-07-14                    83.1                 84.1   \n14 2014-07-15                    74.8                 82.6   \n\n    Maximum Temperature(F)  Total Precipitation(IN)  Mean Wind Speed  \\\n0                     87.3                      0.0             9.09   \n1                     86.9                      0.0             9.78   \n2                     87.1                      0.0             9.55   \n3                     85.5                      0.0             6.21   \n4                     84.6                      0.0             5.29   \n5                     84.2                      0.0             8.29   \n6                     84.7                      0.0             6.90   \n7                     84.6                      0.0             6.10   \n8                     85.1                      0.0             5.29   \n9                     84.4                      0.0             7.25   \n10                    84.6                      0.0             8.29   \n11                    85.1                      0.0            10.01   \n12                    83.7                      0.0             8.98   \n13                    85.3                      0.0             6.79   \n14                    84.6                      0.0             5.52   \n\n    Maximum Sustained Wind Speed  Maximum Wind Gust  \n0                          13.81              999.0  \n1                          17.26              999.0  \n2                          16.11              999.0  \n3                          12.77              999.0  \n4                          10.24              999.0  \n5                          13.81              999.0  \n6                          10.24              999.0  \n7                          11.39              999.0  \n8                          14.96              999.0  \n9                          13.81              999.0  \n10                         14.96              999.0  \n11                         13.81              999.0  \n12                         12.77              999.0  \n13                         10.24              999.0  \n14                         11.39              999.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>Minimum Temperature(F)</th>\n      <th>Mean Temperature(F)</th>\n      <th>Maximum Temperature(F)</th>\n      <th>Total Precipitation(IN)</th>\n      <th>Mean Wind Speed</th>\n      <th>Maximum Sustained Wind Speed</th>\n      <th>Maximum Wind Gust</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2014-07-01</td>\n      <td>77.4</td>\n      <td>81.0</td>\n      <td>87.3</td>\n      <td>0.0</td>\n      <td>9.09</td>\n      <td>13.81</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014-07-02</td>\n      <td>78.3</td>\n      <td>82.8</td>\n      <td>86.9</td>\n      <td>0.0</td>\n      <td>9.78</td>\n      <td>17.26</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2014-07-03</td>\n      <td>82.8</td>\n      <td>84.3</td>\n      <td>87.1</td>\n      <td>0.0</td>\n      <td>9.55</td>\n      <td>16.11</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014-07-04</td>\n      <td>82.4</td>\n      <td>84.3</td>\n      <td>85.5</td>\n      <td>0.0</td>\n      <td>6.21</td>\n      <td>12.77</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2014-07-05</td>\n      <td>82.2</td>\n      <td>83.7</td>\n      <td>84.6</td>\n      <td>0.0</td>\n      <td>5.29</td>\n      <td>10.24</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2014-07-06</td>\n      <td>82.8</td>\n      <td>83.4</td>\n      <td>84.2</td>\n      <td>0.0</td>\n      <td>8.29</td>\n      <td>13.81</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2014-07-07</td>\n      <td>81.9</td>\n      <td>83.5</td>\n      <td>84.7</td>\n      <td>0.0</td>\n      <td>6.90</td>\n      <td>10.24</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2014-07-08</td>\n      <td>81.3</td>\n      <td>83.2</td>\n      <td>84.6</td>\n      <td>0.0</td>\n      <td>6.10</td>\n      <td>11.39</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2014-07-09</td>\n      <td>76.3</td>\n      <td>81.1</td>\n      <td>85.1</td>\n      <td>0.0</td>\n      <td>5.29</td>\n      <td>14.96</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2014-07-10</td>\n      <td>78.3</td>\n      <td>81.8</td>\n      <td>84.4</td>\n      <td>0.0</td>\n      <td>7.25</td>\n      <td>13.81</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2014-07-11</td>\n      <td>80.2</td>\n      <td>83.4</td>\n      <td>84.6</td>\n      <td>0.0</td>\n      <td>8.29</td>\n      <td>14.96</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2014-07-12</td>\n      <td>81.9</td>\n      <td>83.8</td>\n      <td>85.1</td>\n      <td>0.0</td>\n      <td>10.01</td>\n      <td>13.81</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2014-07-13</td>\n      <td>75.7</td>\n      <td>80.4</td>\n      <td>83.7</td>\n      <td>0.0</td>\n      <td>8.98</td>\n      <td>12.77</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2014-07-14</td>\n      <td>83.1</td>\n      <td>84.1</td>\n      <td>85.3</td>\n      <td>0.0</td>\n      <td>6.79</td>\n      <td>10.24</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2014-07-15</td>\n      <td>74.8</td>\n      <td>82.6</td>\n      <td>84.6</td>\n      <td>0.0</td>\n      <td>5.52</td>\n      <td>11.39</td>\n      <td>999.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "# 打开爬取到的CSV文件，将日期列按日期自动处理，使用python引擎，避免可能出现的GB2312编码引起的尴尬\n",
    "weather_data = pd.read_csv(open(r'.\\FL-Miami.csv'), parse_dates=['date'], engine='python')\n",
    "\n",
    "# 数据清洗，首先将表格中标为None的值换程pandas认可的空值类型np.nan，再将全部为nan的列丢弃掉，以达到数据清洗的效果\n",
    "weather_data.replace('None', np.nan, inplace=True)\n",
    "weather_data.dropna(axis=1, how='all', inplace = True)\n",
    "# 输出全部信息\n",
    "weather_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     "output_type": "display_data",
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\n"
     },
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "if not os.path.exists('Results'):\n",
    "    os.mkdir('Results')\n",
    "# 循环除了日期列的所有列，为每一列均绘制一个直方图\n",
    "for column in weather_data.columns:\n",
    "    if column in ['date']:\n",
    "        continue\n",
    "    # 创建画布\n",
    "    plt.figure()\n",
    "    # 绘制直方图，每一列的值作为直方图的数据，纵轴均为天数\n",
    "    plt.hist(weather_data[column].values)\n",
    "    plt.title(column)\n",
    "\n",
    "    # 规定每张图的纵轴为天数，横轴为列名\n",
    "    plt.ylabel(r'Days')\n",
    "    plt.xlabel(column)\n",
    "\n",
    "    # 规定y轴坐标精度是2，最小值从0开始，到日期列的总个数为止（可以理解成总的天数）\n",
    "    y_ticks = np.arange(0, weather_data['date'].count(), 2)\n",
    "    plt.yticks(y_ticks)\n",
    "    # 将直方图保存到本地，文件名为列名\n",
    "    plt.savefig(r'Results/{}.png'.format(column))\n",
    "\n",
    "    # 保证温度记录在前面设定的时间段内，由此创建子集weather_data_subset。也就是说我可以导入一个时间范围更大的csv文件从中选取一个我感兴趣的时间段（注意是左闭右开）\n",
    "weather_data_subset = weather_data[weather_data['date'] >= datetime(year=2014, month=7, day=1)]\n",
    "weather_data_subset = weather_data_subset[weather_data_subset['date'] < datetime(year=2014, month=7, day=16)].copy()\n",
    "\n",
    "    # 将日期和所有列的值都放到一起,从而方便分析，其中只处理温度和风向\n",
    "day_order = weather_data_subset['date'].values\n",
    "record_min_temps = weather_data_subset['Minimum Temperature(F)'].values\n",
    "record_mean_temps = weather_data_subset['Mean Temperature(F)'].values\n",
    "record_max_temps = weather_data_subset['Maximum Temperature(F)'].values\n",
    "record_mean_wind_speed = weather_data_subset['Mean Wind Speed'].values\n",
    "record_max_sustained_wind_speed = weather_data_subset['Maximum Sustained Wind Speed'].values\n",
    "record_max_wind_gust = weather_data_subset['Maximum Wind Gust'].values\n",
    "\n",
    "    # 创建一个空的画布，用来画温度\n",
    "plt.figure(figsize=[6.4, 4.8])\n",
    "    # 创建折线图，展示时间段内每天的最高、最低、平均温度\n",
    "plt.plot(day_order, record_max_temps, c='red', alpha=0.5)\n",
    "plt.plot(day_order, record_min_temps, c='blue', alpha=0.5)\n",
    "plt.plot(day_order, record_mean_temps, c='brown', alpha=0.5)\n",
    "    # 填满两条曲线中间的空隙\n",
    "plt.fill_between(day_order, record_max_temps, record_min_temps, facecolor='blue', alpha=0.1)\n",
    "    # 将横坐标旋转30度避免重叠\n",
    "plt.xticks(rotation = 30)\n",
    "    # 规定图例并显示\n",
    "plt.legend(['Maximum Temperature(F)', 'Minimum Temperature(F)', 'Mean Temperature(F)'])\n",
    "plt.savefig(r'Results/Temperature.png')\n",
    "\n",
    "    # 创建另一个空的画布，用于画风速\n",
    "plt.figure(figsize=[6.4, 4.8])\n",
    "    # 创建折线图，展示时间段内每天的最大持续风速、平均风力\n",
    "plt.plot(day_order, record_max_sustained_wind_speed, c='red', alpha=0.5)\n",
    "plt.plot(day_order, record_mean_wind_speed, c='brown', alpha=0.5)\n",
    "    # 填满两条曲线中间的空隙\n",
    "plt.fill_between(day_order, record_max_sustained_wind_speed, record_mean_wind_speed, facecolor='blue', alpha=0.1)\n",
    "    # 将横坐标旋转30度避免重叠\n",
    "plt.xticks(rotation = 30)\n",
    "    # 规定图例并显示\n",
    "plt.legend(['Maximum Sustained Wind Speed', 'Mean Wind Speed'])\n",
    "plt.savefig(r'Results/WindSpeed.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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